Deep Learning - A Number Of Naive Questions About Caffe

March 19, 2024 Post a Comment

I am trying to understand the basics of caffe, in particular to use with python. My understanding is that the model definition (say a given neural net architecture) must be include

Solution 1:

Let's take a look at one of the examples provided with BVLC/caffe: bvlc_reference_caffenet. You'll notice that in fact there are 3'.prototxt' files:

train_val.prototxt: this file describe the net architecture for the training phase.
depoly.prototxt: this file describe the net architecture for test time ("deployment").
solver.prototxt: this file is very small and contains "meta parameters" for training. For example, the learning rate policy, regulariztion etc.

The net architecture represented by train_val.prototxt and deploy.prototxt should be mostly similar. There are few main difference between the two:

Input data: during training one usually use a predefined set of inputs for training/validation. Therefore, train_val usually contains an explicit input layer, e.g., "HDF5Data" layer or a "Data" layer. On the other hand, deploy usually does not know in advance what inputs it will get, it only contains a statement:
```
input:"data"input_shape {
  dim:10dim:3dim:227dim:227
}
```
that declares what input the net expects and what should be its dimensions. Alternatively, One can put an "Input" layer:
```
layer {
  name: "input"
  type: "Input"
  top: "data"
  input_param { shape { dim: 10dim: 3dim: 227dim: 227 } }
}
```
Input labels: during training we supply the net with the "ground truth" expected outputs, this information is obviously not available during deploy.
Loss layers: during training one must define a loss layer. This layer tells the solver in what direction it should tune the parameters at each iteration. This loss compares the net's current prediction to the expected "ground truth". The gradient of the loss is back-propagated to the rest of the net and this is what drives the learning process. During deploy there is no loss and no back-propagation.

In caffe, you supply a train_val.prototxt describing the net, the train/val datasets and the loss. In addition, you supply a solver.prototxt describing the meta parameters for training. The output of the training process is a .caffemodel binary file containing the trained parameters of the net. Once the net was trained, you can use the deploy.prototxt with the .caffemodel parameters to predict outputs for new and unseen inputs.